# US 12,180,829 B2 · Lean mono-ethylene glycol (MEG) concentration with presence of inorganic salt virtual meter > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,180,829 B2 - Document ID: US12180829B2 - Status: Granted - Kind: B2 - Application number: 17/162,431 - Filed: 2021-01-29 - Published: 2022-08-04 - Granted: 2024-12-31 - Earliest claimed: 2021-01-29 - Assignees: Saudi Arabian Oil Company - Applicants: Saudi Arabian Oil Company - Inventors: Megat A. Rithauddeen; Fayez Kharbat - Web page: https://patentgrep.com/patent/US12180829B2 ## CPC - E21B 43/40: Separation associated with re-injection of separated materials {(E21B43/385 takes precedence)} - E21B 49/0875: {determining specific fluid parameters} - G01V 20/00: Geomodelling in general - G06F 13/00: Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units (interface circuits for specific input/output devices G06F3/00 {; multiprogram control therefor G06F9/46}; multiprocessor systems G06F15/16 ) - E21B 37/06: using chemical means for preventing or limiting {, e.g. eliminating,} the deposition of paraffins or like substances - E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits - E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and methods include a method and system used to predict and monitor lean mono-ethylene glycol (MEG) concentrations after a regeneration process. Process simulations are executed in a MEG regeneration system to generate intermediate parameters and a model correlating MEG concentrations and inorganic salt concentrations. A model for estimating a salt concentration build-up rate from spot samples is generated based on the process simulations. A lean MEG concentration with presence of inorganic salts is predicted in the MEG regeneration system using hybrid artificial intelligence and leveraging physics-guided machine learning. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving, by one or more processors and from sensors, spot samples comprising electrical conductivity and density of mono-ethylene glycol (MEG) concentrations in a transmission pipeline of an industrial plant; executing, by the one or more processors, process simulations to generate intermediate parameters and a correlation model correlating the MEG concentrations and inorganic salt concentrations; generating, by the one or more processors, based on the process simulations, a model for estimating a salt concentration build-up rate from the spot samples, the model defining an estimated variation of the MEG concentrations matched over time to plant operating conditions; predicting, by the one or more processors using hybrid artificial intelligence and physics-guided machine learning, a lean MEG concentration with presence of inorganic salts by correlating the estimated variation of the MEG concentrations at an outlet of a MEG regeneration system, to the salt concentration build-up rate, a reboiler operating pressure, and a temperature of the MEG regeneration system, to determine an amount of the lean MEG concentration at the outlet of the MEG regeneration system; and providing, by the one or more processors, an instruction to adjust plant operating conditions to resolve an inhibition of the transmission pipeline. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: generating notifications and escalations to optimize MEG system operation. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein notifications and escalations to optimize MEG system operation comprise notifications and escalations sent to wellhead platforms and used as inputs to adjust parameters during operations. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein predicting the lean MEG concentration comprises estimating a lean MEG concentration expected to be reinjected into an upstream feed gas trunkline for hydrate inhibition. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein executing the process simulations comprises using rich MEG concentrations at a given pressure and temperature and initial dissolved inorganic salts concentration. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising providing an input to a live soft-sensor to correct a lean MEG concentration calculation. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the spot samples comprise data measuring density and electrical conductivity to estimate initial MEG fraction and salt content. ### Claim 8 A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: receiving, from sensors, spot samples comprising electrical conductivity and density of mono-ethylene glycol (MEG) concentrations in a transmission pipeline of an industrial plant; executing, process simulations to generate intermediate parameters and a correlation model correlating the MEG concentrations and inorganic salt concentrations; generating based on the process simulations, a model for estimating a salt concentration build-up rate from the spot samples, the model defining an estimated variation of the MEG concentrations matched over time to plant operating conditions; predicting, using hybrid artificial intelligence and physics-guided machine learning, a lean MEG concentration with presence of inorganic salts by correlating the estimated variation of the MEG concentrations at an outlet of a MEG regeneration system, to the salt concentration build-up rate, a reboiler operating pressure, and a temperature of the MEG regeneration system, to determine an amount of the lean MEG concentration at the outlet of the MEG regeneration system; and providing an instruction to adjust plant operating conditions to resolve an inhibition of the transmission pipeline. ### Claim 9 (depends on 8) The non-transitory, computer-readable medium of claim 8, the operations further comprising: generating notifications and escalations to optimize MEG system operation. ### Claim 10 (depends on 9) The non-transitory, computer-readable medium of claim 9, wherein notifications and escalations to optimize MEG system operation comprise notifications and escalations sent to wellhead platforms and used as inputs to adjust parameters during operations. ### Claim 11 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein predicting the lean MEG concentration comprises estimating a lean MEG concentration expected to be reinjected into an upstream feed gas trunkline for hydrate inhibition. ### Claim 12 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein executing the process simulations comprises using rich MEG concentrations at a given pressure and temperature and initial dissolved inorganic salts concentration. ### Claim 13 (depends on 8) The non-transitory, computer-readable medium of claim 8, the operations further comprising providing an input to a live soft-sensor to correct a lean MEG concentration calculation. ### Claim 14 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein the spot samples comprise data measuring density and electrical conductivity to estimate initial MEG fraction and salt content. ### Claim 15 A computer-implemented system, comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising: receiving, from sensors, spot samples comprising electrical conductivity and density of mono-ethylene glycol (MEG) concentrations in a transmission pipeline of an industrial plant; executing, process simulations to generate intermediate parameters and a correlation model correlating the MEG concentrations and inorganic salt concentrations; generating based on the process simulations, a model for estimating a salt concentration build-up rate from the spot samples, the model defining an estimated variation of the MEG concentrations matched over time to plant operating conditions; predicting, using hybrid artificial intelligence and physics-guided machine learning, a lean MEG concentration with presence of inorganic salts by correlating the estimated variation of the MEG concentrations at an outlet of a MEG regeneration system, to the salt concentration build-up rate, a reboiler operating pressure, and a temperature of the MEG regeneration system, to determine an amount of the lean MEG concentration at the outlet of the MEG regeneration system; and providing an instruction to adjust plant operating conditions to resolve an inhibition of the transmission pipeline. ### Claim 16 (depends on 15) The computer-implemented system of claim 15, the operations further comprising: generating notifications and escalations to optimize MEG system operation. ### Claim 17 (depends on 16) The computer-implemented system of claim 16, wherein notifications and escalations to optimize MEG system operation comprise notifications and escalations sent to wellhead platforms and used as inputs to adjust parameters during operations. ### Claim 18 (depends on 15) The computer-implemented system of claim 15, wherein predicting the lean MEG concentration comprises estimating a lean MEG concentration expected to be reinjected into an upstream feed gas trunkline for hydrate inhibition. ### Claim 19 (depends on 15) The computer-implemented system of claim 15, wherein executing the process simulations comprises using rich MEG concentrations at a given pressure and temperature and initial dissolved inorganic salts concentration. ### Claim 20 (depends on 15) The computer-implemented system of claim 15, the operations further comprising providing an input to a live soft-sensor to correct a lean MEG concentration calculation.